Advanced Machine Learning Techniques to Improve Hydrological Prediction: A Comparative Analysis of Streamflow Prediction Models
The management of water resources depends heavily on hydrological prediction, and advances in machine learning (ML) present prospects for improving predictive modelling capabilities. This study investigates the use of a variety of widely used machine learning algorithms, such as CatBoost, ElasticNet...
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| Format: | Article |
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MDPI AG
2023-07-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/15/14/2572 |
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| author | Vijendra Kumar Naresh Kedam Kul Vaibhav Sharma Darshan J. Mehta Tommaso Caloiero |
| author_facet | Vijendra Kumar Naresh Kedam Kul Vaibhav Sharma Darshan J. Mehta Tommaso Caloiero |
| author_sort | Vijendra Kumar |
| collection | DOAJ |
| description | The management of water resources depends heavily on hydrological prediction, and advances in machine learning (ML) present prospects for improving predictive modelling capabilities. This study investigates the use of a variety of widely used machine learning algorithms, such as CatBoost, ElasticNet, k-Nearest Neighbors (KNN), Lasso, Light Gradient Boosting Machine Regressor (LGBM), Linear Regression (LR), Multilayer Perceptron (MLP), Random Forest (RF), Ridge, Stochastic Gradient Descent (SGD), and the Extreme Gradient Boosting Regression Model (XGBoost), to predict the river inflow of the Garudeshwar watershed, a key element in planning for flood control and water supply. The substantial engineering feature used in the study, which incorporates temporal lag and contextual data based on Indian seasons, leads it distinctiveness. The study concludes that the CatBoost method demonstrated remarkable performance across various metrics, including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R<sup>2</sup>) values, for both training and testing datasets. This was accomplished by an in-depth investigation and model comparison. In contrast to CatBoost, XGBoost and LGBM demonstrated a higher percentage of data points with prediction errors exceeding 35% for moderate inflow numbers above 10,000. CatBoost established itself as a reliable method for hydrological time-series modelling, easily managing both categorical and continuous variables, and thereby greatly enhancing prediction accuracy. The results of this study highlight the value and promise of widely used machine learning algorithms in hydrology and offer valuable insights for academics and industry professionals. |
| first_indexed | 2024-03-11T00:33:39Z |
| format | Article |
| id | doaj.art-d55fd250f8df42349bd8f0b510a9c69d |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-11T00:33:39Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-d55fd250f8df42349bd8f0b510a9c69d2023-11-18T21:47:14ZengMDPI AGWater2073-44412023-07-011514257210.3390/w15142572Advanced Machine Learning Techniques to Improve Hydrological Prediction: A Comparative Analysis of Streamflow Prediction ModelsVijendra Kumar0Naresh Kedam1Kul Vaibhav Sharma2Darshan J. Mehta3Tommaso Caloiero4Department of Civil Engineering, Dr. Vishwanath Karad MIT World Peace University, Pune 411038, Maharashtra, IndiaDepartment of Thermal Engineering and Thermal Engines, Samara National Research University, Moskovskoye Shosse, 34, Samara 443086, RussiaDepartment of Civil Engineering, Dr. Vishwanath Karad MIT World Peace University, Pune 411038, Maharashtra, IndiaDepartment of Civil Engineering, Dr. S. & S. S. Ghandhy Government Engineering College, Surat 395001, Gujarat, IndiaNational Research Council of Italy, Institute for Agricultural and Forest Systems in Mediterranean (CNR-ISAFOM), 87036 Cosenza, ItalyThe management of water resources depends heavily on hydrological prediction, and advances in machine learning (ML) present prospects for improving predictive modelling capabilities. This study investigates the use of a variety of widely used machine learning algorithms, such as CatBoost, ElasticNet, k-Nearest Neighbors (KNN), Lasso, Light Gradient Boosting Machine Regressor (LGBM), Linear Regression (LR), Multilayer Perceptron (MLP), Random Forest (RF), Ridge, Stochastic Gradient Descent (SGD), and the Extreme Gradient Boosting Regression Model (XGBoost), to predict the river inflow of the Garudeshwar watershed, a key element in planning for flood control and water supply. The substantial engineering feature used in the study, which incorporates temporal lag and contextual data based on Indian seasons, leads it distinctiveness. The study concludes that the CatBoost method demonstrated remarkable performance across various metrics, including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R<sup>2</sup>) values, for both training and testing datasets. This was accomplished by an in-depth investigation and model comparison. In contrast to CatBoost, XGBoost and LGBM demonstrated a higher percentage of data points with prediction errors exceeding 35% for moderate inflow numbers above 10,000. CatBoost established itself as a reliable method for hydrological time-series modelling, easily managing both categorical and continuous variables, and thereby greatly enhancing prediction accuracy. The results of this study highlight the value and promise of widely used machine learning algorithms in hydrology and offer valuable insights for academics and industry professionals.https://www.mdpi.com/2073-4441/15/14/2572hydrological forecastingmachine learningstreamflow predictionCatBoostXGBoostriver inflow prediction |
| spellingShingle | Vijendra Kumar Naresh Kedam Kul Vaibhav Sharma Darshan J. Mehta Tommaso Caloiero Advanced Machine Learning Techniques to Improve Hydrological Prediction: A Comparative Analysis of Streamflow Prediction Models Water hydrological forecasting machine learning streamflow prediction CatBoost XGBoost river inflow prediction |
| title | Advanced Machine Learning Techniques to Improve Hydrological Prediction: A Comparative Analysis of Streamflow Prediction Models |
| title_full | Advanced Machine Learning Techniques to Improve Hydrological Prediction: A Comparative Analysis of Streamflow Prediction Models |
| title_fullStr | Advanced Machine Learning Techniques to Improve Hydrological Prediction: A Comparative Analysis of Streamflow Prediction Models |
| title_full_unstemmed | Advanced Machine Learning Techniques to Improve Hydrological Prediction: A Comparative Analysis of Streamflow Prediction Models |
| title_short | Advanced Machine Learning Techniques to Improve Hydrological Prediction: A Comparative Analysis of Streamflow Prediction Models |
| title_sort | advanced machine learning techniques to improve hydrological prediction a comparative analysis of streamflow prediction models |
| topic | hydrological forecasting machine learning streamflow prediction CatBoost XGBoost river inflow prediction |
| url | https://www.mdpi.com/2073-4441/15/14/2572 |
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